Overview

Dataset statistics

Number of variables27
Number of observations4128002
Missing cells4362895
Missing cells (%)3.9%
Duplicate rows5162
Duplicate rows (%)0.1%
Total size in memory881.8 MiB
Average record size in memory224.0 B

Variable types

Categorical8
DateTime2
Numeric16
Unsupported1

Alerts

Dataset has 5162 (0.1%) duplicate rowsDuplicates
VIN has a high cardinality: 3431307 distinct valuesHigh cardinality
TypMot has a high cardinality: 62234 distinct valuesHigh cardinality
TZn has a high cardinality: 6848 distinct valuesHigh cardinality
ObchOznTyp has a high cardinality: 71457 distinct valuesHigh cardinality
Ct has a high cardinality: 138 distinct valuesHigh cardinality
DrTP is highly imbalanced (63.0%)Imbalance
TZn is highly imbalanced (59.4%)Imbalance
DrVoz is highly imbalanced (71.5%)Imbalance
Ct is highly imbalanced (73.3%)Imbalance
VyslSTK is highly imbalanced (75.3%)Imbalance
TypMot has 203616 (4.9%) missing valuesMissing
VyslEmise has 4128002 (100.0%) missing valuesMissing
Zav9 is highly skewed (γ1 = 48.72950902)Skewed
VIN is uniformly distributedUniform
VyslEmise is an unsupported type, check if it needs cleaning or further analysisUnsupported
Km has 322429 (7.8%) zerosZeros
ZavA has 2019459 (48.9%) zerosZeros
ZavB has 3851276 (93.3%) zerosZeros
ZavC has 4083219 (98.9%) zerosZeros
Zav0 has 3823945 (92.6%) zerosZeros
Zav1 has 3001083 (72.7%) zerosZeros
Zav2 has 3797425 (92.0%) zerosZeros
Zav3 has 3831773 (92.8%) zerosZeros
Zav4 has 3165588 (76.7%) zerosZeros
Zav5 has 2657406 (64.4%) zerosZeros
Zav6 has 2423577 (58.7%) zerosZeros
Zav7 has 4062075 (98.4%) zerosZeros
Zav8 has 4085325 (99.0%) zerosZeros
Zav9 has 4124097 (99.9%) zerosZeros

Reproduction

Analysis started2023-04-01 08:20:47.218554
Analysis finished2023-04-01 08:23:50.440616
Duration3 minutes and 3.22 seconds
Software versionydata-profiling vv4.1.2
Download configurationconfig.json

Variables

DrTP
Categorical

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.0 MiB
pravidelná
2743570 
Evidenční kontrola
922300 
opakovaná
 
218887
Před registrací
 
195861
Na žádost zákazníka
 
19804
Other values (9)
 
27580

Length

Max length46
Median length10
Mean length12.106796
Min length3

Characters and Unicode

Total characters49976878
Distinct characters37
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowEvidenční kontrola
2nd rowpravidelná
3rd rowpravidelná
4th rowpravidelná
5th rowpravidelná

Common Values

ValueCountFrequency (%)
pravidelná 2743570
66.5%
Evidenční kontrola 922300
 
22.3%
opakovaná 218887
 
5.3%
Před registrací 195861
 
4.7%
Na žádost zákazníka 19804
 
0.5%
Technická silniční kontrola 9814
 
0.2%
Před schvál. tech. způsob. vozidla 6211
 
0.2%
ADR 4709
 
0.1%
Před registrací - opakovaná 4118
 
0.1%
TSK - Opakovaná 1133
 
< 0.1%
Other values (4) 1595
 
< 0.1%

Length

2023-04-01T10:23:50.529059image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pravidelná 2743570
51.3%
kontrola 932114
 
17.4%
evidenční 922300
 
17.2%
opakovaná 225625
 
4.2%
před 206371
 
3.9%
registrací 199979
 
3.7%
na 19804
 
0.4%
žádost 19804
 
0.4%
zákazníka 19804
 
0.4%
technická 9922
 
0.2%
Other values (12) 51552
 
1.0%

Most occurring characters

ValueCountFrequency (%)
n 5795371
11.6%
a 4392933
8.8%
e 4088642
8.2%
r 4075750
 
8.2%
v 3904279
 
7.8%
i 3901791
 
7.8%
d 3898545
 
7.8%
l 3698390
 
7.4%
á 3025225
 
6.1%
p 2976729
 
6.0%
Other values (27) 10219223
20.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 47544045
95.1%
Space Separator 1222843
 
2.4%
Uppercase Letter 1184076
 
2.4%
Other Punctuation 19176
 
< 0.1%
Dash Punctuation 6738
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 5795371
12.2%
a 4392933
9.2%
e 4088642
8.6%
r 4075750
8.6%
v 3904279
8.2%
i 3901791
8.2%
d 3898545
8.2%
l 3698390
7.8%
á 3025225
 
6.4%
p 2976729
 
6.3%
Other values (14) 7786390
16.4%
Uppercase Letter
ValueCountFrequency (%)
E 922300
77.9%
P 206371
 
17.4%
N 20946
 
1.8%
T 11981
 
1.0%
D 6015
 
0.5%
A 4981
 
0.4%
R 4981
 
0.4%
S 2167
 
0.2%
K 2167
 
0.2%
O 2167
 
0.2%
Space Separator
ValueCountFrequency (%)
1222843
100.0%
Other Punctuation
ValueCountFrequency (%)
. 19176
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6738
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 48728121
97.5%
Common 1248757
 
2.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 5795371
11.9%
a 4392933
9.0%
e 4088642
8.4%
r 4075750
8.4%
v 3904279
8.0%
i 3901791
8.0%
d 3898545
8.0%
l 3698390
7.6%
á 3025225
 
6.2%
p 2976729
 
6.1%
Other values (24) 8970466
18.4%
Common
ValueCountFrequency (%)
1222843
97.9%
. 19176
 
1.5%
- 6738
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 44634751
89.3%
None 5342127
 
10.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 5795371
13.0%
a 4392933
9.8%
e 4088642
9.2%
r 4075750
9.1%
v 3904279
8.7%
i 3901791
8.7%
d 3898545
8.7%
l 3698390
8.3%
p 2976729
6.7%
o 2347041
5.3%
Other values (21) 5555280
12.4%
None
ValueCountFrequency (%)
á 3025225
56.6%
í 1152113
 
21.6%
č 932114
 
17.4%
ř 206479
 
3.9%
ž 19804
 
0.4%
ů 6392
 
0.1%

VIN
Categorical

HIGH CARDINALITY  UNIFORM 

Distinct3431307
Distinct (%)83.1%
Missing0
Missing (%)0.0%
Memory size63.0 MiB
TEST0000000000001
 
46
001
 
41
002
 
32
004
 
29
008
 
28
Other values (3431302)
4127826 

Length

Max length22
Median length17
Mean length16.488432
Min length1

Characters and Unicode

Total characters68064282
Distinct characters47
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2848327 ?
Unique (%)69.0%

Sample

1st rowTK9AC031141CR2081
2nd rowTM1V023106S000027
3rd row1/12-1801
4th row1/12-1802
5th rowTK9AC071731CR2013

Common Values

ValueCountFrequency (%)
TEST0000000000001 46
 
< 0.1%
001 41
 
< 0.1%
002 32
 
< 0.1%
004 29
 
< 0.1%
008 28
 
< 0.1%
003 27
 
< 0.1%
015 26
 
< 0.1%
006 24
 
< 0.1%
016 23
 
< 0.1%
101 23
 
< 0.1%
Other values (3431297) 4127703
> 99.9%

Length

2023-04-01T10:23:50.716223image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
test0000000000001 46
 
< 0.1%
001 41
 
< 0.1%
002 32
 
< 0.1%
004 29
 
< 0.1%
008 28
 
< 0.1%
003 27
 
< 0.1%
015 26
 
< 0.1%
006 24
 
< 0.1%
101 23
 
< 0.1%
112 23
 
< 0.1%
Other values (3431234) 4127749
> 99.9%

Most occurring characters

ValueCountFrequency (%)
0 6972814
 
10.2%
1 5448543
 
8.0%
2 4281007
 
6.3%
3 3999895
 
5.9%
5 3565070
 
5.2%
6 3482265
 
5.1%
4 3476987
 
5.1%
7 3181384
 
4.7%
8 2945345
 
4.3%
Z 2840310
 
4.2%
Other values (37) 27870662
40.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40079625
58.9%
Uppercase Letter 27907341
41.0%
Dash Punctuation 42648
 
0.1%
Other Punctuation 34616
 
0.1%
Space Separator 46
 
< 0.1%
Math Symbol 5
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
Z 2840310
 
10.2%
B 2236817
 
8.0%
F 1960471
 
7.0%
W 1931245
 
6.9%
M 1747569
 
6.3%
A 1722776
 
6.2%
T 1718753
 
6.2%
V 1401776
 
5.0%
J 1157855
 
4.1%
X 1107729
 
4.0%
Other values (17) 10082040
36.1%
Decimal Number
ValueCountFrequency (%)
0 6972814
17.4%
1 5448543
13.6%
2 4281007
10.7%
3 3999895
10.0%
5 3565070
8.9%
6 3482265
8.7%
4 3476987
8.7%
7 3181384
7.9%
8 2945345
7.3%
9 2726315
 
6.8%
Other Punctuation
ValueCountFrequency (%)
/ 33511
96.8%
. 626
 
1.8%
% 372
 
1.1%
* 95
 
0.3%
, 11
 
< 0.1%
\ 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 42648
100.0%
Space Separator
ValueCountFrequency (%)
46
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40156941
59.0%
Latin 27907341
41.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
Z 2840310
 
10.2%
B 2236817
 
8.0%
F 1960471
 
7.0%
W 1931245
 
6.9%
M 1747569
 
6.3%
A 1722776
 
6.2%
T 1718753
 
6.2%
V 1401776
 
5.0%
J 1157855
 
4.1%
X 1107729
 
4.0%
Other values (17) 10082040
36.1%
Common
ValueCountFrequency (%)
0 6972814
17.4%
1 5448543
13.6%
2 4281007
10.7%
3 3999895
10.0%
5 3565070
8.9%
6 3482265
8.7%
4 3476987
8.7%
7 3181384
7.9%
8 2945345
7.3%
9 2726315
 
6.8%
Other values (10) 77316
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68064281
> 99.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6972814
 
10.2%
1 5448543
 
8.0%
2 4281007
 
6.3%
3 3999895
 
5.9%
5 3565070
 
5.2%
6 3482265
 
5.1%
4 3476987
 
5.1%
7 3181384
 
4.7%
8 2945345
 
4.3%
Z 2840310
 
4.2%
Other values (36) 27870661
40.9%
None
ValueCountFrequency (%)
Č 1
100.0%
Distinct927350
Distinct (%)22.5%
Missing0
Missing (%)0.0%
Memory size63.0 MiB
Minimum2019-01-02 06:09:03.083000
Maximum2019-12-31 00:00:00
2023-04-01T10:23:50.815515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:51.056002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TypMot
Categorical

HIGH CARDINALITY  MISSING 

Distinct62234
Distinct (%)1.6%
Missing203616
Missing (%)4.9%
Memory size63.0 MiB
-
 
103026
BXE
 
36894
ALH
 
36572
781.136M
 
33173
G4FA
 
27526
Other values (62229)
3687195 

Length

Max length17
Median length16
Mean length4.5564004
Min length1

Characters and Unicode

Total characters17881074
Distinct characters103
Distinct categories12 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30897 ?
Unique (%)0.8%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
- 103026
 
2.5%
BXE 36894
 
0.9%
ALH 36572
 
0.9%
781.136M 33173
 
0.8%
G4FA 27526
 
0.7%
BME 26226
 
0.6%
ASV 25709
 
0.6%
AGR 25303
 
0.6%
AZQ 24526
 
0.6%
CBZA 23660
 
0.6%
Other values (62224) 3561771
86.3%
(Missing) 203616
 
4.9%

Length

2023-04-01T10:23:51.158801image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
109640
 
2.5%
bxe 36895
 
0.8%
alh 36626
 
0.8%
7 35731
 
0.8%
m 34338
 
0.8%
781.136m 33521
 
0.8%
781.136 29756
 
0.7%
g4fa 27527
 
0.6%
d 26394
 
0.6%
bme 26227
 
0.6%
Other values (46455) 4028121
91.0%

Most occurring characters

ValueCountFrequency (%)
A 1380118
 
7.7%
1 1128887
 
6.3%
B 933609
 
5.2%
F 876311
 
4.9%
4 852976
 
4.8%
0 837351
 
4.7%
C 790588
 
4.4%
D 773957
 
4.3%
2 612400
 
3.4%
6 584094
 
3.3%
Other values (93) 9110783
51.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10544995
59.0%
Decimal Number 6262851
35.0%
Space Separator 502132
 
2.8%
Other Punctuation 343320
 
1.9%
Dash Punctuation 222265
 
1.2%
Math Symbol 2419
 
< 0.1%
Open Punctuation 1208
 
< 0.1%
Close Punctuation 1180
 
< 0.1%
Lowercase Letter 693
 
< 0.1%
Connector Punctuation 5
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 1380118
 
13.1%
B 933609
 
8.9%
F 876311
 
8.3%
C 790588
 
7.5%
D 773957
 
7.3%
E 531443
 
5.0%
H 504873
 
4.8%
M 497754
 
4.7%
Z 393690
 
3.7%
K 381224
 
3.6%
Other values (28) 3481428
33.0%
Lowercase Letter
ValueCountFrequency (%)
a 74
 
10.7%
c 67
 
9.7%
b 61
 
8.8%
d 44
 
6.3%
f 39
 
5.6%
z 34
 
4.9%
s 33
 
4.8%
š 29
 
4.2%
x 26
 
3.8%
l 23
 
3.3%
Other values (19) 263
38.0%
Other Punctuation
ValueCountFrequency (%)
. 288566
84.1%
/ 38012
 
11.1%
, 10127
 
2.9%
* 6404
 
1.9%
? 162
 
< 0.1%
; 23
 
< 0.1%
: 15
 
< 0.1%
" 5
 
< 0.1%
\ 4
 
< 0.1%
@ 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
1 1128887
18.0%
4 852976
13.6%
0 837351
13.4%
2 612400
9.8%
6 584094
9.3%
3 510718
8.2%
7 495795
7.9%
8 471008
7.5%
9 410013
 
6.5%
5 359609
 
5.7%
Math Symbol
ValueCountFrequency (%)
+ 2417
99.9%
× 1
 
< 0.1%
= 1
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 1204
99.7%
[ 3
 
0.2%
{ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 1177
99.7%
] 2
 
0.2%
} 1
 
0.1%
Modifier Symbol
ValueCountFrequency (%)
´ 2
50.0%
¨ 2
50.0%
Space Separator
ValueCountFrequency (%)
502132
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 222265
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 5
100.0%
Other Symbol
ValueCountFrequency (%)
° 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10545688
59.0%
Common 7335386
41.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 1380118
 
13.1%
B 933609
 
8.9%
F 876311
 
8.3%
C 790588
 
7.5%
D 773957
 
7.3%
E 531443
 
5.0%
H 504873
 
4.8%
M 497754
 
4.7%
Z 393690
 
3.7%
K 381224
 
3.6%
Other values (57) 3482121
33.0%
Common
ValueCountFrequency (%)
1 1128887
15.4%
4 852976
11.6%
0 837351
11.4%
2 612400
8.3%
6 584094
8.0%
3 510718
7.0%
502132
6.8%
7 495795
6.8%
8 471008
6.4%
9 410013
 
5.6%
Other values (26) 930012
12.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17874964
> 99.9%
None 6110
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 1380118
 
7.7%
1 1128887
 
6.3%
B 933609
 
5.2%
F 876311
 
4.9%
4 852976
 
4.8%
0 837351
 
4.7%
C 790588
 
4.4%
D 773957
 
4.3%
2 612400
 
3.4%
6 584094
 
3.3%
Other values (74) 9104673
50.9%
None
ValueCountFrequency (%)
Š 4556
74.6%
Č 1045
 
17.1%
Á 161
 
2.6%
Ý 125
 
2.0%
Í 71
 
1.2%
Ř 49
 
0.8%
š 29
 
0.5%
Ž 22
 
0.4%
ý 20
 
0.3%
Ě 13
 
0.2%
Other values (9) 19
 
0.3%

TZn
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct6848
Distinct (%)0.2%
Missing68
Missing (%)< 0.1%
Memory size63.0 MiB
ŠKODA
1016316 
FORD
282609 
VW
 
201240
RENAULT
 
198127
PEUGEOT
 
195721
Other values (6843)
2233921 

Length

Max length30
Median length29
Mean length5.7474131
Min length1

Characters and Unicode

Total characters23724942
Distinct characters116
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3116 ?
Unique (%)0.1%

Sample

1st rowACK
2nd rowACK
3rd rowACK
4th rowACK
5th rowACK

Common Values

ValueCountFrequency (%)
ŠKODA 1016316
24.6%
FORD 282609
 
6.8%
VW 201240
 
4.9%
RENAULT 198127
 
4.8%
PEUGEOT 195721
 
4.7%
VOLKSWAGEN 183881
 
4.5%
CITROËN 141992
 
3.4%
OPEL 122517
 
3.0%
MERCEDES-BENZ 122248
 
3.0%
HYUNDAI 111212
 
2.7%
Other values (6838) 1552071
37.6%

Length

2023-04-01T10:23:51.263870image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
škoda 1016332
23.9%
ford 282627
 
6.7%
vw 201240
 
4.7%
renault 198239
 
4.7%
peugeot 195732
 
4.6%
volkswagen 183881
 
4.3%
citroën 141992
 
3.3%
opel 122517
 
2.9%
mercedes-benz 122262
 
2.9%
hyundai 111213
 
2.6%
Other values (6432) 1672569
39.4%

Most occurring characters

ValueCountFrequency (%)
A 2870102
 
12.1%
O 2647062
 
11.2%
D 1882193
 
7.9%
E 1778370
 
7.5%
K 1400737
 
5.9%
R 1122819
 
4.7%
N 1119952
 
4.7%
T 1101682
 
4.6%
Š 1019574
 
4.3%
I 928535
 
3.9%
Other values (106) 7853916
33.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23433514
98.8%
Space Separator 136214
 
0.6%
Dash Punctuation 131882
 
0.6%
Lowercase Letter 9873
 
< 0.1%
Other Punctuation 6656
 
< 0.1%
Decimal Number 6623
 
< 0.1%
Math Symbol 160
 
< 0.1%
Modifier Symbol 7
 
< 0.1%
Close Punctuation 6
 
< 0.1%
Open Punctuation 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2870102
 
12.2%
O 2647062
 
11.3%
D 1882193
 
8.0%
E 1778370
 
7.6%
K 1400737
 
6.0%
R 1122819
 
4.8%
N 1119952
 
4.8%
T 1101682
 
4.7%
Š 1019574
 
4.4%
I 928535
 
4.0%
Other values (39) 7562488
32.3%
Lowercase Letter
ValueCountFrequency (%)
a 1202
12.2%
n 1052
 
10.7%
o 991
 
10.0%
e 936
 
9.5%
r 675
 
6.8%
k 487
 
4.9%
m 463
 
4.7%
l 432
 
4.4%
c 383
 
3.9%
s 378
 
3.8%
Other values (32) 2874
29.1%
Decimal Number
ValueCountFrequency (%)
1 1589
24.0%
0 1278
19.3%
5 1128
17.0%
2 782
11.8%
7 488
 
7.4%
3 458
 
6.9%
6 404
 
6.1%
8 229
 
3.5%
4 147
 
2.2%
9 120
 
1.8%
Other Punctuation
ValueCountFrequency (%)
. 5976
89.8%
& 259
 
3.9%
, 240
 
3.6%
/ 177
 
2.7%
* 2
 
< 0.1%
§ 1
 
< 0.1%
' 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 131878
> 99.9%
4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
136214
100.0%
Math Symbol
ValueCountFrequency (%)
+ 160
100.0%
Modifier Symbol
ValueCountFrequency (%)
´ 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23443387
98.8%
Common 281555
 
1.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2870102
 
12.2%
O 2647062
 
11.3%
D 1882193
 
8.0%
E 1778370
 
7.6%
K 1400737
 
6.0%
R 1122819
 
4.8%
N 1119952
 
4.8%
T 1101682
 
4.7%
Š 1019574
 
4.3%
I 928535
 
4.0%
Other values (81) 7572361
32.3%
Common
ValueCountFrequency (%)
136214
48.4%
- 131878
46.8%
. 5976
 
2.1%
1 1589
 
0.6%
0 1278
 
0.5%
5 1128
 
0.4%
2 782
 
0.3%
7 488
 
0.2%
3 458
 
0.2%
6 404
 
0.1%
Other values (15) 1360
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22461508
94.7%
None 1263430
 
5.3%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2870102
12.8%
O 2647062
 
11.8%
D 1882193
 
8.4%
E 1778370
 
7.9%
K 1400737
 
6.2%
R 1122819
 
5.0%
N 1119952
 
5.0%
T 1101682
 
4.9%
I 928535
 
4.1%
S 851337
 
3.8%
Other values (64) 6758719
30.1%
None
ValueCountFrequency (%)
Š 1019574
80.7%
Ë 141996
 
11.2%
Í 33018
 
2.6%
Ý 31768
 
2.5%
Ü 12071
 
1.0%
Á 6885
 
0.5%
Ö 6050
 
0.5%
Č 5541
 
0.4%
Ě 1666
 
0.1%
Ř 1634
 
0.1%
Other values (31) 3227
 
0.3%
Punctuation
ValueCountFrequency (%)
4
100.0%

DrVoz
Categorical

Distinct43
Distinct (%)< 0.1%
Missing2
Missing (%)< 0.1%
Memory size63.0 MiB
OSOBNÍ AUTOMOBIL
3041492 
NÁKLADNÍ AUTOMOBIL
489928 
NÁKLADNÍ PŘÍVĚS
 
178712
MOTOCYKL
 
165848
NÁKLADNÍ NÁVĚS
 
50054
Other values (38)
 
201966

Length

Max length30
Median length16
Mean length15.788628
Min length5

Characters and Unicode

Total characters65175458
Distinct characters33
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowNÁKLADNÍ PŘÍVĚS
2nd rowNÁKLADNÍ PŘÍVĚS
3rd rowNÁKLADNÍ PŘÍVĚS
4th rowNÁKLADNÍ PŘÍVĚS
5th rowNÁKLADNÍ PŘÍVĚS

Common Values

ValueCountFrequency (%)
OSOBNÍ AUTOMOBIL 3041492
73.7%
NÁKLADNÍ AUTOMOBIL 489928
 
11.9%
NÁKLADNÍ PŘÍVĚS 178712
 
4.3%
MOTOCYKL 165848
 
4.0%
NÁKLADNÍ NÁVĚS 50054
 
1.2%
PŘÍPOJNÉ VOZIDLO 33114
 
0.8%
SPECIÁLNÍ AUTOMOBIL 22104
 
0.5%
AUTOBUS 21761
 
0.5%
TRAKTOR 20941
 
0.5%
TRAKTOR KOLOVÝ 19139
 
0.5%
Other values (33) 84907
 
2.1%

Length

2023-04-01T10:23:51.362303image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
automobil 3553524
44.0%
osobní 3041492
37.7%
nákladní 726170
 
9.0%
přívěs 205764
 
2.5%
motocykl 165848
 
2.1%
návěs 57441
 
0.7%
vozidlo 53646
 
0.7%
traktor 40134
 
0.5%
přípojné 33114
 
0.4%
speciální 32383
 
0.4%
Other values (36) 161738
 
2.0%

Most occurring characters

ValueCountFrequency (%)
O 13845462
21.2%
B 6617364
10.2%
N 4677095
 
7.2%
L 4570390
 
7.0%
A 4420483
 
6.8%
Í 4079489
 
6.3%
3943254
 
6.1%
T 3909083
 
6.0%
M 3725207
 
5.7%
I 3639567
 
5.6%
Other values (23) 11748064
18.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 61227931
93.9%
Space Separator 3943254
 
6.1%
Other Punctuation 4273
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 13845462
22.6%
B 6617364
10.8%
N 4677095
 
7.6%
L 4570390
 
7.5%
A 4420483
 
7.2%
Í 4079489
 
6.7%
T 3909083
 
6.4%
M 3725207
 
6.1%
I 3639567
 
5.9%
U 3616968
 
5.9%
Other values (21) 8126823
13.3%
Space Separator
ValueCountFrequency (%)
3943254
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4273
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 61227931
93.9%
Common 3947527
 
6.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 13845462
22.6%
B 6617364
10.8%
N 4677095
 
7.6%
L 4570390
 
7.5%
A 4420483
 
7.2%
Í 4079489
 
6.7%
T 3909083
 
6.4%
M 3725207
 
6.1%
I 3639567
 
5.9%
U 3616968
 
5.9%
Other values (21) 8126823
13.3%
Common
ValueCountFrequency (%)
3943254
99.9%
. 4273
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59579723
91.4%
None 5595735
 
8.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 13845462
23.2%
B 6617364
11.1%
N 4677095
 
7.9%
L 4570390
 
7.7%
A 4420483
 
7.4%
3943254
 
6.6%
T 3909083
 
6.6%
M 3725207
 
6.3%
I 3639567
 
6.1%
U 3616968
 
6.1%
Other values (13) 6614850
11.1%
None
ValueCountFrequency (%)
Í 4079489
72.9%
Á 846264
 
15.1%
Ě 278706
 
5.0%
Ř 243403
 
4.3%
Ý 45240
 
0.8%
É 38016
 
0.7%
Č 34514
 
0.6%
Ů 15049
 
0.3%
Š 14966
 
0.3%
Ž 88
 
< 0.1%

ObchOznTyp
Categorical

Distinct71457
Distinct (%)1.7%
Missing90
Missing (%)< 0.1%
Memory size63.0 MiB
OCTAVIA
 
139900
FABIA
 
118688
OCTAVIA (1Z)
 
100261
FELICIA
 
80296
FABIA (6Y)
 
74303
Other values (71452)
3614464 

Length

Max length40
Median length33
Mean length8.0553701
Min length1

Characters and Unicode

Total characters33251859
Distinct characters118
Distinct categories11 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35864 ?
Unique (%)0.9%

Sample

1st row112040VJB
2nd row2000
3rd row2500
4th row2500
5th rowVIZ POZNÁMKA

Common Values

ValueCountFrequency (%)
OCTAVIA 139900
 
3.4%
FABIA 118688
 
2.9%
OCTAVIA (1Z) 100261
 
2.4%
FELICIA 80296
 
1.9%
FABIA (6Y) 74303
 
1.8%
FABIA (5J) 73458
 
1.8%
OCTAVIA (1U) 58680
 
1.4%
OCTAVIA (5E) 51365
 
1.2%
GOLF 42800
 
1.0%
FABIA COMBI (6Y) 39199
 
0.9%
Other values (71447) 3348962
81.1%

Length

2023-04-01T10:23:51.468291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
octavia 397049
 
6.1%
fabia 333485
 
5.1%
combi 127873
 
1.9%
6y 116982
 
1.8%
1z 114682
 
1.7%
felicia 98795
 
1.5%
5j 97124
 
1.5%
golf 96941
 
1.5%
passat 90150
 
1.4%
focus 82692
 
1.3%
Other values (38047) 5002935
76.3%

Most occurring characters

ValueCountFrequency (%)
A 3708597
 
11.2%
2580221
 
7.8%
I 1913834
 
5.8%
O 1755360
 
5.3%
T 1579005
 
4.7%
C 1512176
 
4.5%
( 1378911
 
4.1%
) 1378707
 
4.1%
R 1344370
 
4.0%
S 1288873
 
3.9%
Other values (108) 14811805
44.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 23160249
69.7%
Decimal Number 4373159
 
13.2%
Space Separator 2580221
 
7.8%
Open Punctuation 1378911
 
4.1%
Close Punctuation 1378707
 
4.1%
Dash Punctuation 155232
 
0.5%
Other Punctuation 119864
 
0.4%
Lowercase Letter 82461
 
0.2%
Modifier Symbol 22037
 
0.1%
Math Symbol 1015
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 3708597
16.0%
I 1913834
 
8.3%
O 1755360
 
7.6%
T 1579005
 
6.8%
C 1512176
 
6.5%
R 1344370
 
5.8%
S 1288873
 
5.6%
E 1270206
 
5.5%
N 956146
 
4.1%
F 894506
 
3.9%
Other values (36) 6937176
30.0%
Lowercase Letter
ValueCountFrequency (%)
i 44748
54.3%
a 4473
 
5.4%
r 4033
 
4.9%
o 3861
 
4.7%
x 2931
 
3.6%
e 2730
 
3.3%
n 2589
 
3.1%
t 2234
 
2.7%
s 1734
 
2.1%
d 1518
 
1.8%
Other values (29) 11610
 
14.1%
Other Punctuation
ValueCountFrequency (%)
. 70544
58.9%
/ 43885
36.6%
, 2179
 
1.8%
* 2100
 
1.8%
! 1045
 
0.9%
' 68
 
0.1%
& 24
 
< 0.1%
" 5
 
< 0.1%
@ 4
 
< 0.1%
; 4
 
< 0.1%
Other values (3) 6
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
0 854903
19.5%
1 705757
16.1%
5 534561
12.2%
3 530713
12.1%
2 505106
11.6%
6 416496
9.5%
4 295039
 
6.7%
7 225770
 
5.2%
8 199341
 
4.6%
9 105473
 
2.4%
Modifier Symbol
ValueCountFrequency (%)
´ 22034
> 99.9%
¨ 2
 
< 0.1%
` 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 155228
> 99.9%
4
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2580221
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1378911
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1378707
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1015
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 23242710
69.9%
Common 10009149
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 3708597
16.0%
I 1913834
 
8.2%
O 1755360
 
7.6%
T 1579005
 
6.8%
C 1512176
 
6.5%
R 1344370
 
5.8%
S 1288873
 
5.5%
E 1270206
 
5.5%
N 956146
 
4.1%
F 894506
 
3.8%
Other values (75) 7019637
30.2%
Common
ValueCountFrequency (%)
2580221
25.8%
( 1378911
13.8%
) 1378707
13.8%
0 854903
 
8.5%
1 705757
 
7.1%
5 534561
 
5.3%
3 530713
 
5.3%
2 505106
 
5.0%
6 416496
 
4.2%
4 295039
 
2.9%
Other values (23) 828735
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33164569
99.7%
None 87286
 
0.3%
Punctuation 4
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 3708597
 
11.2%
2580221
 
7.8%
I 1913834
 
5.8%
O 1755360
 
5.3%
T 1579005
 
4.8%
C 1512176
 
4.6%
( 1378911
 
4.2%
) 1378707
 
4.2%
R 1344370
 
4.1%
S 1288873
 
3.9%
Other values (72) 14724515
44.4%
None
ValueCountFrequency (%)
Ý 23338
26.7%
Í 22181
25.4%
´ 22034
25.2%
Á 14209
16.3%
É 2055
 
2.4%
á 647
 
0.7%
í 568
 
0.7%
Č 561
 
0.6%
ý 450
 
0.5%
Ü 364
 
0.4%
Other values (25) 879
 
1.0%
Punctuation
ValueCountFrequency (%)
4
100.0%

Ct
Categorical

HIGH CARDINALITY  IMBALANCE 

Distinct138
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size63.0 MiB
M1
2965601 
N1
304017 
O1
 
146034
N3
 
118431
O4
 
83218
Other values (133)
510701 

Length

Max length7
Median length2
Mean length2.0618738
Min length1

Characters and Unicode

Total characters8511419
Distinct characters35
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)< 0.1%

Sample

1st rowO2
2nd rowO2
3rd rowO2
4th rowO2
5th rowO2

Common Values

ValueCountFrequency (%)
M1 2965601
71.8%
N1 304017
 
7.4%
O1 146034
 
3.5%
N3 118431
 
2.9%
O4 83218
 
2.0%
M1G 83170
 
2.0%
LC 71669
 
1.7%
L3e 64520
 
1.6%
N2 63206
 
1.5%
O2 48052
 
1.2%
Other values (128) 180084
 
4.4%

Length

2023-04-01T10:23:51.566596image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
m1 2965601
71.8%
n1 304017
 
7.4%
o1 146034
 
3.5%
n3 118431
 
2.9%
o4 83218
 
2.0%
m1g 83170
 
2.0%
lc 71669
 
1.7%
l3e 64520
 
1.6%
n2 63206
 
1.5%
o2 48052
 
1.2%
Other values (124) 180084
 
4.4%

Most occurring characters

ValueCountFrequency (%)
1 3572524
42.0%
M 3071978
36.1%
N 535718
 
6.3%
O 299366
 
3.5%
3 240946
 
2.8%
L 171200
 
2.0%
G 131407
 
1.5%
2 120050
 
1.4%
4 99286
 
1.2%
e 79715
 
0.9%
Other values (25) 189229
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4382710
51.5%
Decimal Number 4037074
47.4%
Lowercase Letter 85563
 
1.0%
Dash Punctuation 5564
 
0.1%
Space Separator 375
 
< 0.1%
Other Punctuation 133
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 3071978
70.1%
N 535718
 
12.2%
O 299366
 
6.8%
L 171200
 
3.9%
G 131407
 
3.0%
C 71743
 
1.6%
T 57711
 
1.3%
A 17729
 
0.4%
S 9604
 
0.2%
E 6626
 
0.2%
Other values (7) 9628
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 3572524
88.5%
3 240946
 
6.0%
2 120050
 
3.0%
4 99286
 
2.5%
7 3002
 
0.1%
5 936
 
< 0.1%
6 330
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
e 79715
93.2%
a 5298
 
6.2%
b 531
 
0.6%
z 12
 
< 0.1%
s 3
 
< 0.1%
p 2
 
< 0.1%
n 2
 
< 0.1%
Other Punctuation
ValueCountFrequency (%)
. 129
97.0%
* 4
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 5564
100.0%
Space Separator
ValueCountFrequency (%)
375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4468273
52.5%
Common 4043146
47.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
M 3071978
68.8%
N 535718
 
12.0%
O 299366
 
6.7%
L 171200
 
3.8%
G 131407
 
2.9%
e 79715
 
1.8%
C 71743
 
1.6%
T 57711
 
1.3%
A 17729
 
0.4%
S 9604
 
0.2%
Other values (14) 22102
 
0.5%
Common
ValueCountFrequency (%)
1 3572524
88.4%
3 240946
 
6.0%
2 120050
 
3.0%
4 99286
 
2.5%
- 5564
 
0.1%
7 3002
 
0.1%
5 936
 
< 0.1%
375
 
< 0.1%
6 330
 
< 0.1%
. 129
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8511419
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3572524
42.0%
M 3071978
36.1%
N 535718
 
6.3%
O 299366
 
3.5%
3 240946
 
2.8%
L 171200
 
2.0%
G 131407
 
1.5%
2 120050
 
1.4%
4 99286
 
1.2%
e 79715
 
0.9%
Other values (25) 189229
 
2.2%
Distinct21913
Distinct (%)0.5%
Missing15501
Missing (%)0.4%
Memory size63.0 MiB
Minimum1753-01-01 00:00:00
Maximum2030-12-17 00:00:00
2023-04-01T10:23:51.656913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:51.754370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Km
Real number (ℝ)

Distinct517723
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean162823.22
Minimum0
Maximum9714321
Zeros322429
Zeros (%)7.8%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:51.859116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q164427
median149824
Q3227292
95-th percentile370513.95
Maximum9714321
Range9714321
Interquartile range (IQR)162865

Descriptive statistics

Standard deviation142975.16
Coefficient of variation (CV)0.87810052
Kurtosis104.37921
Mean162823.22
Median Absolute Deviation (MAD)81374
Skewness4.3715156
Sum6.7213459 × 1011
Variance2.0441896 × 1010
MonotonicityNot monotonic
2023-04-01T10:23:51.955828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 322429
 
7.8%
1 1980
 
< 0.1%
3 780
 
< 0.1%
10 760
 
< 0.1%
12 749
 
< 0.1%
8 730
 
< 0.1%
11 719
 
< 0.1%
14 699
 
< 0.1%
9 685
 
< 0.1%
7 643
 
< 0.1%
Other values (517713) 3797828
92.0%
ValueCountFrequency (%)
0 322429
7.8%
1 1980
 
< 0.1%
2 523
 
< 0.1%
3 780
 
< 0.1%
4 611
 
< 0.1%
5 559
 
< 0.1%
6 517
 
< 0.1%
7 643
 
< 0.1%
8 730
 
< 0.1%
9 685
 
< 0.1%
ValueCountFrequency (%)
9714321 1
< 0.1%
9654883 1
< 0.1%
9144155 1
< 0.1%
9012063 1
< 0.1%
9002236 1
< 0.1%
8960163 1
< 0.1%
8783685 1
< 0.1%
8737118 1
< 0.1%
8345159 1
< 0.1%
8311701 1
< 0.1%

VyslSTK
Categorical

Distinct3
Distinct (%)< 0.1%
Missing115
Missing (%)< 0.1%
Memory size63.0 MiB
způsobilé
3842572 
částečně způsobilé
 
262148
nezpůsobilé
 
23167

Length

Max length18
Median length9
Mean length9.5827839
Min length9

Characters and Unicode

Total characters39556649
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowčástečně způsobilé
2nd rowzpůsobilé
3rd rowzpůsobilé
4th rowzpůsobilé
5th rowzpůsobilé

Common Values

ValueCountFrequency (%)
způsobilé 3842572
93.1%
částečně způsobilé 262148
 
6.4%
nezpůsobilé 23167
 
0.6%
(Missing) 115
 
< 0.1%

Length

2023-04-01T10:23:52.036973image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-04-01T10:23:52.140046image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
způsobilé 4104720
93.5%
částečně 262148
 
6.0%
nezpůsobilé 23167
 
0.5%

Most occurring characters

ValueCountFrequency (%)
s 4390035
11.1%
z 4127887
10.4%
p 4127887
10.4%
ů 4127887
10.4%
o 4127887
10.4%
b 4127887
10.4%
i 4127887
10.4%
l 4127887
10.4%
é 4127887
10.4%
č 524296
 
1.3%
Other values (6) 1619222
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 39294501
99.3%
Space Separator 262148
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4390035
11.2%
z 4127887
10.5%
p 4127887
10.5%
ů 4127887
10.5%
o 4127887
10.5%
b 4127887
10.5%
i 4127887
10.5%
l 4127887
10.5%
é 4127887
10.5%
č 524296
 
1.3%
Other values (5) 1357074
 
3.5%
Space Separator
ValueCountFrequency (%)
262148
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 39294501
99.3%
Common 262148
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4390035
11.2%
z 4127887
10.5%
p 4127887
10.5%
ů 4127887
10.5%
o 4127887
10.5%
b 4127887
10.5%
i 4127887
10.5%
l 4127887
10.5%
é 4127887
10.5%
č 524296
 
1.3%
Other values (5) 1357074
 
3.5%
Common
ValueCountFrequency (%)
262148
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 30252283
76.5%
None 9304366
 
23.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4390035
14.5%
z 4127887
13.6%
p 4127887
13.6%
o 4127887
13.6%
b 4127887
13.6%
i 4127887
13.6%
l 4127887
13.6%
e 285315
 
0.9%
n 285315
 
0.9%
t 262148
 
0.9%
None
ValueCountFrequency (%)
ů 4127887
44.4%
é 4127887
44.4%
č 524296
 
5.6%
á 262148
 
2.8%
ě 262148
 
2.8%

VyslEmise
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing4128002
Missing (%)100.0%
Memory size63.0 MiB

DTKont
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9039574
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.199050image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile5
Maximum7
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4070416
Coefficient of variation (CV)0.48452557
Kurtosis-1.1111617
Mean2.9039574
Median Absolute Deviation (MAD)1
Skewness0.1455658
Sum11987542
Variance1.9797661
MonotonicityNot monotonic
2023-04-01T10:23:52.255383image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 889186
21.5%
3 881828
21.4%
2 851743
20.6%
4 831473
20.1%
5 620306
15.0%
6 52298
 
1.3%
7 1168
 
< 0.1%
ValueCountFrequency (%)
1 889186
21.5%
2 851743
20.6%
3 881828
21.4%
4 831473
20.1%
5 620306
15.0%
6 52298
 
1.3%
7 1168
 
< 0.1%
ValueCountFrequency (%)
7 1168
 
< 0.1%
6 52298
 
1.3%
5 620306
15.0%
4 831473
20.1%
3 881828
21.4%
2 851743
20.6%
1 889186
21.5%

ZavA
Real number (ℝ)

Distinct33
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6662497
Minimum0
Maximum39
Zeros2019459
Zeros (%)48.9%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.332666image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile6
Maximum39
Range39
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.249679
Coefficient of variation (CV)1.3501452
Kurtosis3.2500054
Mean1.6662497
Median Absolute Deviation (MAD)1
Skewness1.6232747
Sum6878282
Variance5.0610557
MonotonicityNot monotonic
2023-04-01T10:23:52.416092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 2019459
48.9%
1 503557
 
12.2%
2 435232
 
10.5%
3 369701
 
9.0%
4 299772
 
7.3%
5 202663
 
4.9%
6 122929
 
3.0%
7 75056
 
1.8%
8 44051
 
1.1%
9 27769
 
0.7%
Other values (23) 27813
 
0.7%
ValueCountFrequency (%)
0 2019459
48.9%
1 503557
 
12.2%
2 435232
 
10.5%
3 369701
 
9.0%
4 299772
 
7.3%
5 202663
 
4.9%
6 122929
 
3.0%
7 75056
 
1.8%
8 44051
 
1.1%
9 27769
 
0.7%
ValueCountFrequency (%)
39 1
 
< 0.1%
38 1
 
< 0.1%
31 2
 
< 0.1%
30 1
 
< 0.1%
28 4
 
< 0.1%
27 3
 
< 0.1%
26 6
 
< 0.1%
25 12
< 0.1%
24 25
< 0.1%
23 24
< 0.1%

ZavB
Real number (ℝ)

Distinct32
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16624241
Minimum0
Maximum60
Zeros3851276
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.506092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum60
Range60
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.8032659
Coefficient of variation (CV)4.8318952
Kurtosis84.564936
Mean0.16624241
Median Absolute Deviation (MAD)0
Skewness7.4022598
Sum686249
Variance0.64523611
MonotonicityNot monotonic
2023-04-01T10:23:52.586245image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 3851276
93.3%
1 116776
 
2.8%
2 60410
 
1.5%
3 39402
 
1.0%
4 24687
 
0.6%
5 14486
 
0.4%
6 8110
 
0.2%
7 4997
 
0.1%
8 3059
 
0.1%
9 1867
 
< 0.1%
Other values (22) 2932
 
0.1%
ValueCountFrequency (%)
0 3851276
93.3%
1 116776
 
2.8%
2 60410
 
1.5%
3 39402
 
1.0%
4 24687
 
0.6%
5 14486
 
0.4%
6 8110
 
0.2%
7 4997
 
0.1%
8 3059
 
0.1%
9 1867
 
< 0.1%
ValueCountFrequency (%)
60 1
 
< 0.1%
44 2
 
< 0.1%
31 2
 
< 0.1%
29 2
 
< 0.1%
28 1
 
< 0.1%
27 1
 
< 0.1%
25 3
< 0.1%
24 4
< 0.1%
23 2
 
< 0.1%
22 6
< 0.1%

ZavC
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013075091
Minimum0
Maximum15
Zeros4083219
Zeros (%)98.9%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.667089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.14009122
Coefficient of variation (CV)10.714359
Kurtosis471.55314
Mean0.013075091
Median Absolute Deviation (MAD)0
Skewness16.311631
Sum53974
Variance0.019625549
MonotonicityNot monotonic
2023-04-01T10:23:52.732264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 4083219
98.9%
1 38452
 
0.9%
2 4442
 
0.1%
3 1305
 
< 0.1%
4 389
 
< 0.1%
5 109
 
< 0.1%
6 43
 
< 0.1%
7 18
 
< 0.1%
8 10
 
< 0.1%
9 8
 
< 0.1%
Other values (4) 7
 
< 0.1%
ValueCountFrequency (%)
0 4083219
98.9%
1 38452
 
0.9%
2 4442
 
0.1%
3 1305
 
< 0.1%
4 389
 
< 0.1%
5 109
 
< 0.1%
6 43
 
< 0.1%
7 18
 
< 0.1%
8 10
 
< 0.1%
9 8
 
< 0.1%
ValueCountFrequency (%)
15 2
 
< 0.1%
14 1
 
< 0.1%
11 2
 
< 0.1%
10 2
 
< 0.1%
9 8
 
< 0.1%
8 10
 
< 0.1%
7 18
 
< 0.1%
6 43
 
< 0.1%
5 109
 
< 0.1%
4 389
< 0.1%

Zav0
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08006876
Minimum0
Maximum6
Zeros3823945
Zeros (%)92.6%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.804661image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum6
Range6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.29553003
Coefficient of variation (CV)3.6909531
Kurtosis17.597407
Mean0.08006876
Median Absolute Deviation (MAD)0
Skewness3.9666999
Sum330524
Variance0.087338
MonotonicityNot monotonic
2023-04-01T10:23:52.865977image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 3823945
92.6%
1 279238
 
6.8%
2 23279
 
0.6%
3 1444
 
< 0.1%
4 85
 
< 0.1%
5 10
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
0 3823945
92.6%
1 279238
 
6.8%
2 23279
 
0.6%
3 1444
 
< 0.1%
4 85
 
< 0.1%
5 10
 
< 0.1%
6 1
 
< 0.1%
ValueCountFrequency (%)
6 1
 
< 0.1%
5 10
 
< 0.1%
4 85
 
< 0.1%
3 1444
 
< 0.1%
2 23279
 
0.6%
1 279238
 
6.8%
0 3823945
92.6%

Zav1
Real number (ℝ)

Distinct14
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.41361656
Minimum0
Maximum14
Zeros3001083
Zeros (%)72.7%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:52.941446image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum14
Range14
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79529721
Coefficient of variation (CV)1.9227886
Kurtosis7.1938082
Mean0.41361656
Median Absolute Deviation (MAD)0
Skewness2.3647425
Sum1707410
Variance0.63249765
MonotonicityNot monotonic
2023-04-01T10:23:53.011098image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 3001083
72.7%
1 707788
 
17.1%
2 301011
 
7.3%
3 87967
 
2.1%
4 21406
 
0.5%
5 5916
 
0.1%
6 1857
 
< 0.1%
7 642
 
< 0.1%
8 202
 
< 0.1%
9 81
 
< 0.1%
Other values (4) 49
 
< 0.1%
ValueCountFrequency (%)
0 3001083
72.7%
1 707788
 
17.1%
2 301011
 
7.3%
3 87967
 
2.1%
4 21406
 
0.5%
5 5916
 
0.1%
6 1857
 
< 0.1%
7 642
 
< 0.1%
8 202
 
< 0.1%
9 81
 
< 0.1%
ValueCountFrequency (%)
14 2
 
< 0.1%
12 3
 
< 0.1%
11 10
 
< 0.1%
10 34
 
< 0.1%
9 81
 
< 0.1%
8 202
 
< 0.1%
7 642
 
< 0.1%
6 1857
 
< 0.1%
5 5916
 
0.1%
4 21406
0.5%

Zav2
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.08772864
Minimum0
Maximum9
Zeros3797425
Zeros (%)92.0%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.089966image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31089842
Coefficient of variation (CV)3.5438646
Kurtosis17.37425
Mean0.08772864
Median Absolute Deviation (MAD)0
Skewness3.8652037
Sum362144
Variance0.096657825
MonotonicityNot monotonic
2023-04-01T10:23:53.150654image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3797425
92.0%
1 301405
 
7.3%
2 27056
 
0.7%
3 1889
 
< 0.1%
4 191
 
< 0.1%
5 23
 
< 0.1%
6 12
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
0 3797425
92.0%
1 301405
 
7.3%
2 27056
 
0.7%
3 1889
 
< 0.1%
4 191
 
< 0.1%
5 23
 
< 0.1%
6 12
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
6 12
 
< 0.1%
5 23
 
< 0.1%
4 191
 
< 0.1%
3 1889
 
< 0.1%
2 27056
 
0.7%
1 301405
 
7.3%
0 3797425
92.0%

Zav3
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.082946907
Minimum0
Maximum7
Zeros3831773
Zeros (%)92.8%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.213210image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31939542
Coefficient of variation (CV)3.8506007
Kurtosis25.276967
Mean0.082946907
Median Absolute Deviation (MAD)0
Skewness4.5206155
Sum342405
Variance0.10201344
MonotonicityNot monotonic
2023-04-01T10:23:53.276842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 3831773
92.8%
1 256404
 
6.2%
2 34351
 
0.8%
3 4729
 
0.1%
4 630
 
< 0.1%
5 99
 
< 0.1%
6 15
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 3831773
92.8%
1 256404
 
6.2%
2 34351
 
0.8%
3 4729
 
0.1%
4 630
 
< 0.1%
5 99
 
< 0.1%
6 15
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 15
 
< 0.1%
5 99
 
< 0.1%
4 630
 
< 0.1%
3 4729
 
0.1%
2 34351
 
0.8%
1 256404
 
6.2%
0 3831773
92.8%

Zav4
Real number (ℝ)

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.32724161
Minimum0
Maximum18
Zeros3165588
Zeros (%)76.7%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.353198image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum18
Range18
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.69848047
Coefficient of variation (CV)2.1344489
Kurtosis12.760654
Mean0.32724161
Median Absolute Deviation (MAD)0
Skewness2.9016474
Sum1350854
Variance0.48787497
MonotonicityNot monotonic
2023-04-01T10:23:53.424522image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 3165588
76.7%
1 682818
 
16.5%
2 205309
 
5.0%
3 51991
 
1.3%
4 14786
 
0.4%
5 4696
 
0.1%
6 1739
 
< 0.1%
7 619
 
< 0.1%
8 273
 
< 0.1%
9 89
 
< 0.1%
Other values (8) 94
 
< 0.1%
ValueCountFrequency (%)
0 3165588
76.7%
1 682818
 
16.5%
2 205309
 
5.0%
3 51991
 
1.3%
4 14786
 
0.4%
5 4696
 
0.1%
6 1739
 
< 0.1%
7 619
 
< 0.1%
8 273
 
< 0.1%
9 89
 
< 0.1%
ValueCountFrequency (%)
18 1
 
< 0.1%
16 1
 
< 0.1%
15 8
 
< 0.1%
14 2
 
< 0.1%
13 6
 
< 0.1%
12 12
 
< 0.1%
11 25
 
< 0.1%
10 39
 
< 0.1%
9 89
 
< 0.1%
8 273
< 0.1%

Zav5
Real number (ℝ)

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46942225
Minimum0
Maximum11
Zeros2657406
Zeros (%)64.4%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.511004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.72320251
Coefficient of variation (CV)1.5406226
Kurtosis3.1988614
Mean0.46942225
Median Absolute Deviation (MAD)0
Skewness1.6572237
Sum1937776
Variance0.52302188
MonotonicityNot monotonic
2023-04-01T10:23:53.579501image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 2657406
64.4%
1 1084479
26.3%
2 318312
 
7.7%
3 57485
 
1.4%
4 8132
 
0.2%
5 1625
 
< 0.1%
6 429
 
< 0.1%
7 95
 
< 0.1%
8 28
 
< 0.1%
9 9
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0 2657406
64.4%
1 1084479
26.3%
2 318312
 
7.7%
3 57485
 
1.4%
4 8132
 
0.2%
5 1625
 
< 0.1%
6 429
 
< 0.1%
7 95
 
< 0.1%
8 28
 
< 0.1%
9 9
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 1
 
< 0.1%
9 9
 
< 0.1%
8 28
 
< 0.1%
7 95
 
< 0.1%
6 429
 
< 0.1%
5 1625
 
< 0.1%
4 8132
 
0.2%
3 57485
 
1.4%
2 318312
7.7%

Zav6
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.74800012
Minimum0
Maximum20
Zeros2423577
Zeros (%)58.7%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.658377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.1303411
Coefficient of variation (CV)1.511151
Kurtosis5.4319162
Mean0.74800012
Median Absolute Deviation (MAD)0
Skewness1.9490202
Sum3087746
Variance1.2776711
MonotonicityNot monotonic
2023-04-01T10:23:53.736746image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 2423577
58.7%
1 855378
 
20.7%
2 513043
 
12.4%
3 213406
 
5.2%
4 77702
 
1.9%
5 27556
 
0.7%
6 10110
 
0.2%
7 4010
 
0.1%
8 1760
 
< 0.1%
9 716
 
< 0.1%
Other values (11) 744
 
< 0.1%
ValueCountFrequency (%)
0 2423577
58.7%
1 855378
 
20.7%
2 513043
 
12.4%
3 213406
 
5.2%
4 77702
 
1.9%
5 27556
 
0.7%
6 10110
 
0.2%
7 4010
 
0.1%
8 1760
 
< 0.1%
9 716
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
17 3
 
< 0.1%
16 7
 
< 0.1%
15 12
 
< 0.1%
14 32
 
< 0.1%
13 43
 
< 0.1%
12 91
< 0.1%
11 193
< 0.1%

Zav7
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.017156726
Minimum0
Maximum8
Zeros4062075
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:53.816822image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.1400044
Coefficient of variation (CV)8.1603216
Kurtosis113.62813
Mean0.017156726
Median Absolute Deviation (MAD)0
Skewness9.3955419
Sum70823
Variance0.019601233
MonotonicityNot monotonic
2023-04-01T10:23:53.886719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4062075
98.4%
1 61622
 
1.5%
2 3837
 
0.1%
3 378
 
< 0.1%
4 64
 
< 0.1%
5 21
 
< 0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
0 4062075
98.4%
1 61622
 
1.5%
2 3837
 
0.1%
3 378
 
< 0.1%
4 64
 
< 0.1%
5 21
 
< 0.1%
6 4
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
6 4
 
< 0.1%
5 21
 
< 0.1%
4 64
 
< 0.1%
3 378
 
< 0.1%
2 3837
 
0.1%
1 61622
 
1.5%
0 4062075
98.4%

Zav8
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012620876
Minimum0
Maximum7
Zeros4085325
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:54.106928image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.13301207
Coefficient of variation (CV)10.539052
Kurtosis195.49611
Mean0.012620876
Median Absolute Deviation (MAD)0
Skewness12.691302
Sum52099
Variance0.017692211
MonotonicityNot monotonic
2023-04-01T10:23:54.168001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 4085325
99.0%
1 34494
 
0.8%
2 7061
 
0.2%
3 1020
 
< 0.1%
4 89
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4085325
99.0%
1 34494
 
0.8%
2 7061
 
0.2%
3 1020
 
< 0.1%
4 89
 
< 0.1%
5 12
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
5 12
 
< 0.1%
4 89
 
< 0.1%
3 1020
 
< 0.1%
2 7061
 
0.2%
1 34494
 
0.8%
0 4085325
99.0%

Zav9
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0013187978
Minimum0
Maximum8
Zeros4124097
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size63.0 MiB
2023-04-01T10:23:54.237786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum8
Range8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.048411837
Coefficient of variation (CV)36.709067
Kurtosis3086.4732
Mean0.0013187978
Median Absolute Deviation (MAD)0
Skewness48.729509
Sum5444
Variance0.0023437059
MonotonicityNot monotonic
2023-04-01T10:23:54.303133image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 4124097
99.9%
1 2800
 
0.1%
2 776
 
< 0.1%
3 253
 
< 0.1%
4 55
 
< 0.1%
5 16
 
< 0.1%
6 3
 
< 0.1%
8 1
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
0 4124097
99.9%
1 2800
 
0.1%
2 776
 
< 0.1%
3 253
 
< 0.1%
4 55
 
< 0.1%
5 16
 
< 0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
ValueCountFrequency (%)
8 1
 
< 0.1%
7 1
 
< 0.1%
6 3
 
< 0.1%
5 16
 
< 0.1%
4 55
 
< 0.1%
3 253
 
< 0.1%
2 776
 
< 0.1%
1 2800
 
0.1%
0 4124097
99.9%

StariDnu
Real number (ℝ)

Distinct21913
Distinct (%)0.5%
Missing15501
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean6235.021
Minimum-2897
Maximum98625
Zeros0
Zeros (%)0.0%
Negative13
Negative (%)< 0.1%
Memory size63.0 MiB
2023-04-01T10:23:54.396329image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum-2897
5-th percentile2338
Q14149
median5888
Q37707
95-th percentile11333
Maximum98625
Range101522
Interquartile range (IQR)3558

Descriptive statistics

Standard deviation3603.4241
Coefficient of variation (CV)0.57793295
Kurtosis191.1564
Mean6235.021
Median Absolute Deviation (MAD)1773
Skewness8.4089651
Sum2.564153 × 1010
Variance12984665
MonotonicityNot monotonic
2023-04-01T10:23:54.499216image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9141 8184
 
0.2%
8776 8114
 
0.2%
9506 8070
 
0.2%
8411 7103
 
0.2%
9872 6230
 
0.2%
10237 4810
 
0.1%
8045 4654
 
0.1%
12063 3920
 
0.1%
10602 3846
 
0.1%
11333 3717
 
0.1%
Other values (21903) 4053853
98.2%
(Missing) 15501
 
0.4%
ValueCountFrequency (%)
-2897 2
< 0.1%
-2868 1
< 0.1%
-2787 1
< 0.1%
-2782 1
< 0.1%
-2735 1
< 0.1%
-2731 1
< 0.1%
-2728 1
< 0.1%
-2645 1
< 0.1%
-2637 1
< 0.1%
-2576 1
< 0.1%
ValueCountFrequency (%)
98625 1785
< 0.1%
98586 1
 
< 0.1%
98579 2
 
< 0.1%
98502 1
 
< 0.1%
98462 1
 
< 0.1%
98446 1
 
< 0.1%
98443 1
 
< 0.1%
98377 1
 
< 0.1%
98376 1
 
< 0.1%
44935 381
 
< 0.1%

Interactions

2023-04-01T10:23:19.639534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:21:59.547848image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:04.925251image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:10.105493image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:15.655006image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:20.866138image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:26.141757image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:31.542747image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:36.932423image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:42.038029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:47.391419image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:52.832879image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:58.176787image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:03.836865image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:09.010354image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:14.232044image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:20.026631image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:21:59.887154image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:05.221160image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:10.447099image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:15.977122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:21.194097image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:26.463890image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:31.874217image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:37.247786image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:42.357678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:47.725695image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:53.170081image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:58.516365image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:04.156570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:09.328092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:14.554996image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:20.419248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:00.216828image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:05.534553image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:10.781637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:16.299214image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:21.525274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:26.789424image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:32.208640image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:37.562218image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:42.676965image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:48.062571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:53.503322image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:58.860384image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:04.480363image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:09.648664image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:14.871291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:20.821064image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:00.698018image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:05.853606image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:11.127511image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:16.606335image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:21.864588image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:27.123440image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:32.548800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:37.882545image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:43.010437image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:48.408295image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:53.836200image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:59.214893image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:04.806615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:09.977077image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:15.340439image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:21.208334image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:01.018579image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:06.168181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:11.460027image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:16.928489image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:22.172436image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:27.448792image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:32.882167image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:38.199323image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:43.337209image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:48.747891image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:54.161820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:59.553743image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:05.125246image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:10.297291image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:15.656131image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:21.608989image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:01.341866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:06.490990image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:11.795836image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:17.257418image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:22.505571image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:27.758090image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:33.216719image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:38.521075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:43.662831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:49.089618image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:54.488421image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:59.896324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:05.451399image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:10.627258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:15.975712image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:22.003991image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:01.660944image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:06.806704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:12.127602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:17.581385image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:22.829122image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:28.079938image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:33.533812image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:38.837636image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:43.985290image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:49.426775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:54.812453image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:00.240713image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:05.771481image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:10.948679image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:16.298287image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:22.397411image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:01.980660image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:07.136552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:12.462273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:17.903390image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:23.158585image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:28.407739image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:33.865407image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:39.148532image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:44.304132image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:49.767127image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:55.141779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:00.590107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:06.093011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:11.276377image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:16.623184image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:22.785644image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:02.292040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:07.451883image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:12.792312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:18.218827image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:23.488258image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:28.719586image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:34.194369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:39.458993image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:44.602515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:50.092534image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:55.461515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:00.919488image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:06.405628image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:11.597349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:16.939869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:23.183404image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:02.614974image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:07.775898image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:13.127933image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:18.547425image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:23.814117image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:29.042358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:34.535369image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:39.778461image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:44.920677image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:50.417124image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:55.794643image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:01.395808image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:06.728800image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:11.926071image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:17.258552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:23.583842image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:02.939305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:08.103831image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:13.464410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:18.874302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:24.144142image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:29.373113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:34.873514image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:40.102211image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:45.243610image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:50.754740image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:56.121516image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:01.737700image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:07.051840image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:12.249289image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:17.580452image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:23.970567image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:03.261710image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:08.424556image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:13.794092image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:19.198844image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:24.469557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:29.698896image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:35.211969image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:40.407614image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:45.557183image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:51.088195image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:56.458721image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:02.065449image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:07.363327image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:12.572274image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:17.897544image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:24.371087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:03.592100image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:08.756002image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:14.133552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:19.527615image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:24.800889image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:30.030602image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:35.554621image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:40.731987image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:46.037181image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:51.425947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:56.792761image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:02.423835image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:07.672866image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:12.904356image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:18.219995image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:24.779259image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:03.921275image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:09.079438image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:14.472557image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:19.857029image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:25.130515image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:30.507763image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:35.892570image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:41.053129image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:46.358766image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:51.765775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:57.122678image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:02.771143image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:07.999374image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:13.219164image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:18.541934image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:25.178552image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:04.243994image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:09.395613image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:14.802709image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:20.175537image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:25.451589image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:30.834256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:36.227986image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:41.368196image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:46.684942image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:52.103519image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:57.452910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:03.119741image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:08.324153image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:13.543371image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:18.859576image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:25.551954image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:04.615794image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:09.769370image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:15.325769image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:20.542302image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:25.816775image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:31.205910image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:36.612089image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:41.720391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:47.053285image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:52.490684image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:22:57.835107image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:03.514040image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:08.688877image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:13.917264image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-04-01T10:23:19.240292image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Missing values

2023-04-01T10:23:27.914468image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-04-01T10:23:33.608874image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-04-01T10:23:44.559010image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
3749Evidenční kontrolaTK9AC031141CR20812019-01-14 11:32:39.500NaNACKNÁKLADNÍ PŘÍVĚS112040VJBO22004-07-260částečně způsobiléNaN101010000000006743.0
3411pravidelnáTM1V023106S0000272019-01-31 13:03:49.427NaNACKNÁKLADNÍ PŘÍVĚS2000O22007-06-040způsobiléNaN410000001000005700.0
3212pravidelná1/12-18012019-01-16 10:55:43.637-ACKNÁKLADNÍ PŘÍVĚS2500O21998-06-240způsobiléNaN320001000010008967.0
3116pravidelná1/12-18022019-01-22 09:59:10.230-ACKNÁKLADNÍ PŘÍVĚS2500O21998-07-020způsobiléNaN200000000000008959.0
3622pravidelnáTK9AC071731CR20132019-01-09 19:05:17.577NaNACKNÁKLADNÍ PŘÍVĚSVIZ POZNÁMKAO22003-03-170způsobiléNaN310000001000007240.0
3425pravidelná1/12-16342019-01-08 09:36:50.083NaNACKNÁKLADNÍ PŘÍVĚS750O11997-04-030způsobiléNaN200010000000009414.0
3306pravidelnáTK9AC052041CR20752019-01-10 10:31:30.210NaNACKNÁKLADNÍ PŘÍVĚS202040VTBO22004-07-210způsobiléNaN400000000000006748.0
3516pravidelnáWAFZLAF103K0267132019-01-21 17:22:09.637-ACKERMANNNÁKLADNÍ PŘÍVĚSZ.LA-FO32004-12-100způsobiléNaN130002000010006606.0
3822pravidelnáWAFZPAF186K0303352019-01-07 14:18:25.917NaNACKERMANNNÁKLADNÍ PŘÍVĚSZ-PA-F 18/7.6 EO42006-10-140způsobiléNaN140001000120005933.0
3225Před registracíWAFZKAF18DK0366692019-01-22 10:40:29.870NaNACKERMANNPŘÍPOJNÉ VOZIDLOZ-KA-F18/7.1EO42014-01-210částečně způsobiléNaN222001001111003277.0
DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKVyslEmiseDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu
STK
6711pravidelná21392019-12-316901ZETORTRAKTOR KOLOVÝ6911T11978-03-150způsobiléNaN2000000000000016373.0
6711pravidelná48232019-12-31Z 5201ZETORTRAKTOR KOLOVÝ5211T11985-01-290způsobiléNaN2600000040300013861.0
7706Technická silniční kontrolaYS2S4X200055708042019-12-13NaNSCANIANÁKLADNÍ AUTOMOBILN321N3NaT15191způsobiléNaN50000000000000NaN
7706Technická silniční kontrolaWKESD0000004172122019-12-11NaNKRONENÁKLADNÍ NÁVĚSSDP 27O4NaT0způsobiléNaN30000000000000NaN
7706Technická silniční kontrolaWKESDC270512382292019-12-11NaNKRONENÁKLADNÍ NÁVĚS ADRSDP 27O4NaT0způsobiléNaN30000000000000NaN
7706Technická silniční kontrolaTK9235266A2PP70332019-12-06NaNPARAGANNÁKLADNÍ PŘÍVĚSGAPAO2NaT0způsobiléNaN50000000000000NaN
7701Technická silniční kontrolaXLRTE47MS0E9072842019-12-27NaNDAFTAHAČ NÁVĚSŮFTN3NaT0způsobiléNaN50000000000000NaN
7701Technická silniční kontrolaXLRTEH4300G2723042019-12-27NaNDAFTAHAČ NÁVĚSŮXF 460 FTN3NaT0způsobiléNaN50000000000000NaN
7701Technická silniční kontrolaVAVJS1339BH3035902019-12-27NaNSCHWARZMÜLLERNÁKLADNÍ NÁVĚSSPA 3/EO4NaT0způsobiléNaN50000000000000NaN
7701Technická silniční kontrolaWK0S00024001627652019-12-27NaNKÖGELNÁKLADNÍ NÁVĚSSN 24O4NaT0způsobiléNaN50000000000000NaN

Duplicate rows

Most frequently occurring

DrTPVINDatKontTypMotTZnDrVozObchOznTypCtDatPrvRegKmVyslSTKDTKontZavAZavBZavCZav0Zav1Zav2Zav3Zav4Zav5Zav6Zav7Zav8Zav9StariDnu# duplicates
23ADRXLRTEH4300G1616612019-06-05MX-13 340 H1DAFNÁKLADNÍ AUTOMOBILXF 460 FTN32017-09-01197663způsobilé300000000000001958.04
278Evidenční kontrolaTMBEFF653T02549702019-09-11781.136BŠKODAOSOBNÍ AUTOMOBILFELICIA COMBIM11996-03-26145843způsobilé300000000000009787.04
569Evidenční kontrolaWAUZZZ8U2HR0273262019-12-10DFUAUDIOSOBNÍ AUTOMOBILQ3M12016-10-0338105způsobilé200000000000002291.04
2919pravidelnáJN1APUD22U00503552019-08-06YD25NISSANSPECIÁLNÍ AUTOMOBILSINGLE-CABN1G2005-02-10104956způsobilé230001000020006544.04
9ADRW0VMRY603KB1868232019-09-24M9TC7OPELNÁKLADNÍ AUT. ADRMOVANON12019-09-18682způsobilé200000000000001211.03
52Evidenční kontrola17272019-04-16-WT-METALLPŘÍPOJNÉ VOZIDLOTH3O11992-09-240způsobilé2000000000000011066.03
109Evidenční kontrola73-189442019-06-12NaNBSSPŘÍVĚS TRAKTOROVÝP73SOT41975-11-250způsobilé3000100000000017214.03
229Evidenční kontrolaTMAPT81DAKJ3150302019-09-10G4FCHYUNDAIOSOBNÍ AUTOMOBILIX 20M12019-06-2711způsobilé200000000000001294.03
475Evidenční kontrolaVF3WEKFT0BE0838782019-08-15KFTPEUGEOTOSOBNÍ AUTOMOBIL207 (W)M12012-06-1965797způsobilé400000000000003858.03
583Evidenční kontrolaWBAAP91000JH859392019-08-05306D1BMWOSOBNÍ AUTOMOBIL330 (346L)M12000-04-26254423částečně způsobilé101010000000008295.03